Travel Intention to Visit Tourism Destinations: A Perspective of Generation Z in Vietnam

Abstract

The purpose of this research is to investigate the impacts of gen-Z's perception of consumer-generated content on social media on their travel intention with the mediating role of travel motivation push and pull. An online questionnaire survey of a total of 369 samples was conducted with the participation of gen Z in the most important cities across Vietnam. The model was analyzed using Structural Equation Modeling (SEM) using AMOS program 22 to investigate model relationships and all hypotheses are significant. The findings indicated that gen Z values the usefulness of social media and they use social media for knowledge-seeking (push factor), and this leads to their intention to visit a destination. SEM analysis also reveals that gen Z tends to use social media to find accessibility to any destinations and they are motivated highly with destinations that have clear and easy access, for example, no visa requirement or neighboring destinations. As the result, they have better intentions to visit these destinations. This research will help marketers, especially marketing specialists to gain a better understanding of gen Z, thus implement better marketing techniques to target gen Z.

1. Introduction

Tourism is becoming one of the most profitable growth engines for the global economy and the main source of income for many countries in the world. According to the World Travel & Tourism Council (WTTC) in 2019, the tourism industry has accounted for around 11% of the world’s GDP, thus becoming the largest economic sector worldwide. Information technology today has enhanced travelers not only to search for information online but to connect, exchange, and share valuable information based on their experience, for example through social media. Younger generations, especially gen Z, are much more active in planning their trips on the Internet as they search for information for their trips from the beginning to the end of the travel decision-making process.

Social Media is an interactive platform via which users can create, exchange, share, and discuss ideas, opinions, and experiences. The research demonstrated the fact that tourism customers tend to believe information shared in social media more and that considerably reduces their decision-making process (Icoz et al., 2019).

People, especially gen Z increasingly perceive social media applications as an important part of their daily life as they likely interact often with these virtual platforms. This reflects their orientations and behaviors (Alalwan et al., 2017). For Gen Z, social media is primarily used for information, leisure, and entertainment purposes. Their daily lives are completely attached to the Internet as they spend about 10 hours per day online (Livingstone, 2018).

Many studies discussed and analyzed the influential factors of travel intention whereas others demonstrated the important role of information sources on travel motivation. Social media has created great opportunities for the tourism industry, for instance, e-marketing, thus, influencing visit intention through peer-to-peer networking. Each platform of social media can serve a different purpose for tourists such as YouTube offering travel videos, travel channels; Instagram is considered as one of the largest influencers of travel motivation with the images of nice destinations being posted by celebrities/models, travel influencers, and travel bloggers which are more trusted and liked. Despite its importance, researches on how information on social media can affect the travel intention of the young generation, especially gen Z are still limited (Dimitriou & AbouElgheit, 2019).

For this reason, the objectives of this study are as follow:

1) To examine the effectiveness of social media use on travel intention with the mediating role of push and pull factors.

2) To examine how social media affects travel intention of the young generation-gen Z.

2. Literature Review

2.1. Gen Z and Gen Z in Vietnam

Generation Z, or Gen Z for short, is the demographic cohort succeeding the Millennials who were born in the period from 1996–2010 (Cho et al., 2018; Haddouche & Salomone, 2018; Monaco, 2018; Stergiou, 2018). Gen Z is known otherwise as iGen, post-Millennials as young age people tend to be comfortable with the Internet and Social Media, especially in the time of industry 4.0. These individuals are strongly affected by high technology and considered fans of high-end devices such as PC, computer, iPad, iPhone, and smartphones. Thus, gen Z people are living in the age of smart connection with the appearance of a smart city, smart life, smart tourism. Based on the estimation of Nielsen (2018), there will be a total of 2 billion iGenres worldwide, accounting for 33% of the world population by the end of 2025. According to the United Nations Population Fund (UNFPA, 2017), Vietnam is at the golden period of young age from 15–24 with 7.510.600 people, equal to 21% of the national population, and this period will be finished at the end of 2040 (UNFPA, 2017). With a high scale of population, Gen Z has a crucial role in society, in the development process of enterprises and the global economy; besides, they are a big dynamic and creative human resource, especially, they are also a potential market for both private or general sectors.

We are living in a time of renovation, co-creation, and democratization of tourist experiences (Monaco, 2018). Traveling is not only a favorite experience for upper classes but also all sectors in society who have a passion for discovery, learning, relaxing, or building a good relationship in the global community. Especially, along with the sharing economy, the proliferation of connected devices permits all individuals to easily connect, exchange, and travel worldwide. Gen Z will be the future of the world economy, transferring from dependent to independent period to be mature and successful individuals in the community (UNWTO, 2011; Vision Criticial, 2016). Francis and Tracy (2018) also argued that iGen is the generation of people who have a strong influence in society because they usually create new trends in behavior and other experiential activities, which may lead to changes in consumer behavior in the future (Schlossberg, 2016). Therefore, Haddouche and Salomone (2018) stated that Gen Z will create big opportunities and challenges for the tourism and hospitality industry.

2.2. Theoretical Model

2.2.1. Social Media

Social media has been perceived as an important part of daily life for people, especially in this new century of industrialization 4.0. Social media applications are one of the most efficient and influential applications that are engaged in the most important part of human life, i.e. social, educational to political and business life (Alalwan et al., 2017; Zeng & Gerritsen, 2014). People tend to react through virtual platforms such as Facebook, Instagram, LinkedIn, among others. In turn, their behaviors and orientation toward all kinds of social media are reflected (Rathore et al., 2016).

Generally, social media is defined as a group of Internetbased applications on which users such as suppliers, marketers, and consumers can create, share and exchange their content rapidly (Oliveira et al., 2020). Typical types of social media are blogs, social networking sites, virtual social worlds, collaborative projects, content communities, and virtual game worlds (Kaplan & Haenlein, 2009).

In this world of the Internet of Things (IoT), social media is referred to as consumer-generated media. Hence, information posted on social media is perceived to have more credibility compared to other traditional information sources such as websites or advertisements, especially in the tourism industry (Fotis et al., 2012). In other words, social media is perceived usefulness for users; the relationships between social media subscriptions, and perceived usefulness of social media for socializing and for communication, were found to be positive and statistically significant. Perceived usefulness is one of the main constructs of the technology acceptance model (TAM) which can be applied to predict consumer behavior (Galib et al., 2018). For example, TripAdvisor offers information from millions of travelers all over the world, with millions of reviews and recommendations. Thus, TripAdvisor becomes an important information source when tourists plan their trips (Oliveira et al., 2020).

Nowadays, social media users not only search for information to plan their travel but also to share their personal experience after each travel (Fotis et al., 2012). This valuable information from relatives and friends can play a crucial role in decision making because it gives people the trustworthiness and perceived value for the sources of information. The growth of social media and social network sites metamorphosis from traditional word of mouth to electronic word of mouth (eWom). Earlier WOM communication was face-to-face are discussed and shared among known friends and relatives. eWoM is shared among known friends, relatives, and interested communities in social network sites such as Facebook, Twitter, and more sites. Social media build a social network that influences word of mouth on the user buying decision. Social networking sites (SNS) have experienced a boom in the past few years in terms of usage, especially by the younger generation, and as an advertising medium. Firms are investing substantial resources to engage consumers through electronic word of mouth (eWOM), hoping to stimulate purchase intentions (Alhidari et al., 2015). The perceived value concept is a key factor in traditional consumer behavior. Current research has succeeded in demonstrating the role of perceived value on online consumer behavior and its influences on online word of mouth (OWOM) and behavioral loyalty. Mobile Internet (M-Internet) is a new Information and Communication Technology (ICT) from the value perspective. M-Internet is a fast-growing enabling technology for Mobile Commerce. Consumers’ perception of the value of Mobile-Internet is a principal determinant of adoption intention, and the other beliefs are mediated through perceived value (Ajina, 2019; Kim et al., 2007). The Internet has changed the nature of shopping in the past two decades, which has supported the proliferation of e-commerce sites, and thus shopping has shifted to e-shopping. Also, customers use social media to gain information on preferred products with the best price options, as social media provides shoppers a voice, and facilitate them to interact and share their opinion worldwide. Moreover, social media is an extensively adopted platform for e-commerce (Yadav & Rahman, 2017).

Social media and its impact on the customer’s behavior and perception have gained impressive attention in a various number of research articles. Customer’s behavior and perception can be affected and predicted by information posted over these social media platforms (Alalwan et al., 2017; Kakirala & Singh, 2020).

Numerous studies have also demonstrated the influence of information sources on travel motivation (Chetthamrongchai, 2017) whereas consumer motivations to travel can be centered on the push and pull factors (Hillman & Radel, 2018). In the light of previous studies to describe the interrelationship between the pull factors–attributes of a destination and the push factors- motivation on travel decision, this study proposes these hypotheses:

H1: Social Media use significantly influences travel motivation (push factor).

H2: Social Media use significantly influences travel motivation (pull factor).

2.2.2. Travel Motivation and Intention to Visit A Destination

Tourist motivation has been a controversial concept of many researchers in different fields, such as psychology, anthropology, and sociology (Crompton, 1979; Dann, 1977; Pearce & Caltabiano, 1983). Push and Pull theory is the most outstanding one that is considered a fundamental theory for the tourism sector to understand the behavior of tourists and explain why an individual travels (Uysal & Baloglu, 1996). According to Mohammad and Som (2010), “push and pull motivations have been primary utilized in studies of tourists behavior and played as a useful role in understanding a wide variety of needs, wants that can motivate and influence tourists behaviors”. For instance, young tourists in Ghana were pulled by accessibility, historical-cultural attractions, natural-ecological heritage, and service delivery of the destinations, whereas they were pushed by the following factors: rest/relaxation, knowledge-seeking, novelty, and ego-enhancement (Preko et al., 2019).

The review of the tourist motivation literature pointed out that the higher travel motivation a tourist has, the better destination image and higher travel intention he or she has (Chu & Luckanavanich, 2018; Hasan et al., 2018). By understanding travel motivation, we can learn the influential factors that have a strong impact on one’s intention to select a tourist destination (Chu & Luckanavanich, 2018; Hasan et al., 2018; Jang et al., 2009; Khan et al., 2018; Nguyen, 2014).

Hasan et al. (2018) and Lemy et al. (2020) found that when being satisfied with push and pull factors, tourist’s motivation could be linked to their intentions to visit or revisit a destination in the future. Destination marketers should focus on tourists’ motivations because they also can contribute to explaining travel intentions (Jang et al., 2009).

Several studies have proposed a list of motivators as pull factors and push factors related to specific destinations. In the context of tourism in Vietnam, this research considered all the attributes as determinants of tourist’s destination toward intentional visit introduced by Dann (1977, 1981), and his successors like Prayag and Ryan (2011), Chetthamrongchai (2017), Wen and Huang (2019). Since there might be countless attributes and intrinsic motives to explain why tourists choose a destination, only attributes and motives relevant to the specific destination (Vietnam) and characteristics of gen Z have been selected to finalize the independent variable for this study. Hence, pull factors consist of antecedents such as “Affordable” (Prayag, 2010), “Accessibility” (Dȩbski & Nasierowski, 2017; Lu, 2011; Nguyen, 2014), “Recreation services” (Lu, 2011; Usamah & Anuar, 2017), “Image of destination” (Nikjoo & Ketabi, 2015; Wangari et al., 2017); whereas push factors include “Knowledge seeking”, “Ego-Enhancement”, “Socialization” (Seyanont, 2017) & “Escape” (Nikjoo & Ketabi, 2015).

Behavioral intention is seen as a decisive factor in enhancing a destination’s popularity. Thus, the determinants of behavioral intention are frequently explored in tourism research. Behavioral intention can be captured by the intentions to share information and visit a destination. Intention to recommend is the intention to share the experience through social media communications and the intention to visit a destination.

This study concentrated on the push and pull motives of gen Z tourists to exchange information about a destination on social media and their intention to visit as one of the aims of the research. As mentioned above, the following hypotheses were proposed:

H3: Push factors of tourist’s motivation significantly influence travelers’ intention.

H4: Pull factors of tourist’s motivation significantly influence travelers’ intention.

3. Research Methodology

3.1. Data Collection & Sample

Data was collected from a sample of 369 respondents belonging to Gen Z (the age group from 15 to 24 years old). These respondents are high school pupils, students, and college and university graduates across Vietnam.

A pilot study was conducted with 12 students to improve the composition of some of the questions and determine the time needed for the survey. After conducting the pretest, minor changes were made to the wording of the survey. For the survey, university students were contacted to participate in the experiment using purposive sampling. Participants had to meet 3 different criteria: (i) age belongs to gen Z (ii) frequent social media and Internet usage (iii) have the intention to travel.

For analyzing the demographic of 369 participants, the study uses the main value-frequency which is shown in percentage (Table 1).

Table 1: The distribution of responders characteristic

Figure 1: The Research Model

About gender: 120 females (32.5%), 243 males (65.9%) and 15 LGBT (1.6%).

About age: 15 respondents from 15 to 17 years old (4.1%), 308 respondents from 18 to 22 years old (83.5%), 46 respondents from 23 to 24 years old (12.5%).

About Education level: 21 respondents with high school level (5.7%), 27 respondents with Intermediate/ College (7.3%), 321 respondents with a university degree (87%).

About income: There are 169 people (45.8%) depending on family income, 86 people have income under 3 million VND (23.3%), 67 people have income from 3 to 6 million VND (18.2%), 27 people with income from 7 to 10 million VND with (7.3%), 20 people with earnings more than 10 million VND (5.4%).

About traveling: there are 247 people for domestic traveling (66.9%), 6 people for outbound traveling (1.6%), 102 people traveled both domestic and abroad (27.6%) and 14 people have never traveled (3.8%).

About traveling frequency: 124 people traveling once a year (33.6%), 125 people traveling twice a year (33.9%), 53 people traveling 3 times/year (14.4%), and 67 people traveling more than 3 times/year (18.2%).

Expenses for travel: 164 people got support from family (44.4%), 134 people got from their savings: (36.3%), 65 people got from their earnings of part-time job: (17.6%) and 6 people got from other sources (1.6%).

Frequency of using social media: 11 people using less than 10 minutes/day, (3%), 24 people using 10 to 30 minutes/day (6.5%), 66 people using 31 to 60 minutes/ day (17.9%), 102 people spending from 1 to 2 hours on social media / day (27.6%), 93 people spending from 2 hours to 3 hours / day (25.2% rate), 73 people using over 3 hours / day (19.8%).

3.2. Measures

The measures for the research constructs were all adopted from existing literature. The statements used to measure social media was adopted from Fotis et al. (2012), Icoz et al. (2019), and Anjna (2019). It comprised three dimensions: Info trustworthiness, social media usefulness, social media perceived value. The measurement of travel motivation was adopted from Prayag (2010), Nikjoo and Ketabi (2015), and Seyanont (2017).

3.3. Data Analysis Techniques

The data analysis techniques utilized in this research included reliability and validity assessment of the measurement instruments. Cronbach’s Alpha was used to test the reliability of the measures and Confirmatory Factor Analysis (CFA) was used to analyze and refine the models of each construct as well as establish its validity. After that, the research model was analyzed by using Structural Equation Modeling (SEM) to test the hypothesized relationships.

4. Findings

4.1. Reliability and Validity of Constructs

To assess the reliability and validity of the constructs, the Cronbach’s alpha coefficient, mean, composite reliability (CR), and average variance extracted (AVE) are examined in addition to the factor loading of each questionnaire item. Therefore, the following table (Table 2) is presented to summarize the reliability and validity analysis results.

Table 2: Reliability Analysis and Convergent Validity

As the results showed, the significance of correlations indicates that convergent reliability and validity is confirmed. Moreover, alpha coefficients of higher than 0.7, and AVE values which are greater than 0.5 indicated that the research instrument is reliable and valid. Since all values were significant, they are appropriate for the confirmatory factor analysis (CFA). CFA analysis results show that Chi-squared is 1498.889 with df = 968, P = 0.000. Cmin/df = 1.548 < 5 meet the requirement for compatibility. GFI = 0.852, TLI = 0.921 > 0.9, CFI = 0.930 > 0.9 and RMSEA= 0.039 < 0.08 are all suitable.

Other indexes (Table 1):

(1) Convergent validity: The coefficients (standardized) are > 0.5, the unstandardized coeffcients are valid, so the scales attain convergent validity.

(2) Discriminant validity: All P-values < 0.05 so the correlation coefficients of the concepts are not 1 with the reliability of 95%. Therefore, all the concepts attain discriminant validity.

(3) Uni-dimensionality: The model is consistent with the market, and there is no correlation, so it attains uni-dimensionality.

(4) Reliability: reliability test results through the following indexes: (i) composite reliability; (ii) total variance extracted and (ii) Cronbach’s alpha. All the scales have composite reliability > 0.5, total variance extracted > 0.5, Cronbach’s alpha > 0.5, so the scales attain reliability.

4.2. Structural Model and Hypotheses Testing

Structural Equation Modeling (SEM) was used to test the impact of travel motivation of gen Z, the results in Table 2 and Figure 2 as follows:

Figure 2: SEM Model

Analysis results using SEM show that Chi-squared is 1744.294 with df = 1017, P = 0.000. Cmin / df = 1.715 < 5 meet the requirement for compatibility. GFI = 0.832, TLI = 0.898 ~ 0.9, CFI = 0.904 > 0.9 and RMSEA= 0.04412 < 0.08 are all suitable.

To verify the hypotheses which were proposed in the above literature review section, the structural model was formulated using AMOS. The model requires that a set of criterion fit indices should be achieved based on the recommended values. Overall, the final structural model suggested an adequate fit to the collected data where the value of Chi-square (χ²) is 1744.294 with df = 1017 (p-value = 0.000), Cmin / df = 1.715 < 3. Other fit indices also achieved the minimum cut-off values (GFI = 0.832, TLI = 0.898 ~ 0.9, CFI = 0.904, and RMSEA = 0.04412). Based on the above results of criterion values, it can be said that the final structural model has a good fit with the data of this study.

Overall, the presented hypotheses were tested based on the regression table which was generated based on the final structural model’s output. The findings presented in Table 2 indicate that social media has a significant positive relationship with Push factors of travel motivation (β range from 0.485 to 0.784, p < 0.05), and thus, H1 is accepted.

H2 which stated that social media has a significant relationship with pull factors of travel motivation is also accepted (β range from 0.135 to 0.343, p < 0.05).

Table 4: The Results of Bootstrap Testing (N = 1000)

For the next hypothesis, the results indicated a significant relationship between push factors of travel motivation and travel intention (β range from 0.145 to 0.205, p < 0.05), therefore, H3 is also supported.

For the last hypothesis, three items of pull factors (destination image, recreation, and accessibility) indicated a significant relationship with travel intention (β range from 0.129 to 0.158, p < 0.05). Item ‘’affordable’’ shows a week significant relationship with travel intention as (β = 0.010, p = 0.844 > 0.05). In other words, gen Z in Vietnam is not affected by pricing if they think it worth it when they have the intention to travel.

This study is applied bootstrap with the sample n = 1000. The result of deviation showed not statistically significant in Table 3; hence, it can be concluded that this research model is reliable.

Table 3: The Results of Testing the Relationship Among the Concepts (Standardized)

Note: *** = P-value = 0.000.

5. Conclusion and Limitations

The purpose of conducting this research is to analyze the impact of social media use on travel intention with the mediating role of travel motivation (push and pull factors) for young people age from 15–24 years old. This study performs three analyses: The first one is a descriptive analysis to calculate the means of the items. The second analysis is an SEM analysis to examine the causal relationships between the constructs. All 4 hypotheses are accepted and prove a positive relationship among variables, except the item “Affordable” of pull factors.

The findings with SEM indicate that gen Z values the usefulness of social media the most, then comes the perceived value and last information trustworthiness.

The construct “Push factor’’ contains items such as knowledge-seeking, socialization, ego enhancement, escape. Based on the standardized. The total direct and indirect effect on SEM analysis, the effect of social media on knowledge seeking (push factor), and knowledge-seeking intention to visit are the highest (with an indicator of 0.784 and 0.205 accordingly). It means gen Z tends to use social media to gain knowledge and it thus becomes their motivation and intention to visit for a real experience.

The construct “Pull factor” contains items such as accessibility (ACC), recreation services (RSE), affordable (ACC), and image of destination (IMD). SEM analysis reveals that the direct effects of MS on ACC (0.343) and ACC on ITV are the highest. Gen Z tends to use SM to find accessibility to any destinations and they are motivated highly with the destination that has clear and easy access, for example, free visa, or neighboring destinations. As the result, they have a better intention to visit that destination.

Item MS has a direct effect on AFF (0.157) but AFF shows a weak effect on ITV (only 0.01, P-value > 0.05). it can be explained that gen Z values the “Affordable” factor when looking for travel information on social media. However, this item is not on their priority list when they have the intention to visit a destination. Other factors such as ACC (e.g visa-free, easy to access), RSE (for example, creative destination), IDM (for example, international recognized destinations) are more important because it reflects their lifestyle, check-in trend style.

Theoretically, this study contributes to the literature concerning motivation factors and behavioral intention. The other main contribution compared to prior studies is the tension of the theory of planned behavior by adding influences of social media in the research context. However, due to the small sample collected in this study, focus groups are more students at higher education which is a limited variety of respondents. This proposes new approaches for further studies to analyze effects and other functions of social media on destination brand marketing, and their co-creation through social media to add more value for a destination.